Conv Module.
Conditional GANs [1]}, [2]}
have conditional inputs at the first layer. e.g., the source image or latent noise.
An important benefit of our architecture is that it includes conditional inputs at every layer, instead of only having conditional inputs at the first layer.
We observed that if the conditional input is limited to the first layer, then its effect diminishes as the features propagate deeper in the network. As a result, there is hardly any conditional information left in deeper layers.
We decided to feed the conditional inputs at every layer through a Conv module shown in the encoder module of Figure REF .
The Conv Module consists of 3 convolution layers, each followed by a ReLU activation function which outputs the number of features required in the layer under consideration.
